SPEECH RECOGNITION SYSTEM

Lecture Notes in Computer Science, 2015

Applications on mobile devices have been characterized for their usability. The voice is a natural means of interaction between users and mobile devices. Traditional speech recognition algorithms work in controlled media are targeted to specific population groups (e.g. age, gender or language to name of few), and also require a lot of computational resources so that the algorithms are effective. Therefore, pattern recognition is performed in mobile applications as web services. However, this type of solution generates high dependence on Internet connectivity, so it is desirable to have an embedded module for this task that does not consume many computational resources and have a good level of effectiveness. This paper presents an embedded mobile systems for voice recognition module is presented. This module works in noisy environments, it works for any age of users and has proved that it can work for several languages.

2010

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2002

We introduce SRI's new speech recognition engine, DynaSpeak TM , which is characterized by its scalability and flexibility, high recognition accuracy, memory and speed efficiency, adaptation capability, efficient grammar optimization, support for natural language parsing functionality, and operation based on integer arithmetic. These features are designed to address the needs of the fast-developing and changing domain of embedded and mobile computing platforms.

IJCSMC, 2020

There are constant and ongoing research and innovations to enhance speech processes for ease of communication for all, but most especially those with disabilities. With recent technology advancements, voice recognition has gained a massive acceptance particularly, when it comes to making communications with machines more natural and seamless. Voice technologies is increasingly being applied in many electronic devices most especially phones, personal computers, and web browsers. This is achieved mainly using a Speech Recognition System (SRS). In this write-up, some of the models, approaches, Application Programming Interfaces (APIs), performance metrics used in SRS are reviewed. Thereafter, a sample implementation of one of the common APIs, JavaScript Web Speech API, was presented; noting particularly the simplicity with which speech recognition using APIs we can achieved, with just a basic knowledge of programming language.

2008

ASR (Automatic Speech Recognition) is one of key technologies in the upcoming Ubiquitous Computing and Ambient Intelligence. In this paper, first, the surveys on processing devices such as microprocessors and memories, and on communication infrastructure, especially wireless communication infrastructure relating to ASR are reported. Second, the embedded version of CSR (Continuous Speech Recognition) software for the mobile environmental use of ASR is reported.

This paper presents a new research and education platform for speech processing. The platform is called Speech Enhancement for Android (SEA) and incorporates past and present speech enhancement techniques applied to recorded speech corrupted by real world noise sources. Researchers, students and teaching staff can use this platform to perform speech enhancement and observe its effects on the linguistic content of speech. This paper outlines the speech processing strategies implemented in SEA along with discussing the benefits of this new interactive method to present speech processing theory and research.

Industrial Electronics, IEEE …

We present a hardware-software coprocessing speech recognizer for real-time embedded applications. The system consists of a standard microprocessor and a hardware accelerator for Gaussian mixture model (GMM) emission probability calculation implemented on a field-programmable gate array. The GMM accelerator is optimized for timing performance by exploiting data parallelism. In order to avoid large memory requirement, the accelerator adopts a double buffering scheme for accessing the acoustic parameters with no assumption made on the access pattern of these parameters. Experiments on widely used benchmark data show that the real-time factor of the proposed system is 0.62, which is about three times faster than the pure software-based baseline system, while the word accuracy rate is preserved at 93.33%. As a part of the recognizer, a new adaptive beam-pruning algorithm is also proposed and implemented, which further reduces the average real-time factor to 0.54 with the word accuracy rate of 93.16%. The proposed speech recognizer is suitable for integration in various types of voice (speech)-controlled applications.

IEEE Transactions on Industrial Electronics, 2000

We present a hardware-software coprocessing speech recognizer for real-time embedded applications. The system consists of a standard microprocessor and a hardware accelerator for Gaussian mixture model (GMM) emission probability calculation implemented on a field-programmable gate array. The GMM accelerator is optimized for timing performance by exploiting data parallelism. In order to avoid large memory requirement, the accelerator adopts a double buffering scheme for accessing the acoustic parameters with no assumption made on the access pattern of these parameters. Experiments on widely used benchmark data show that the real-time factor of the proposed system is 0.62, which is about three times faster than the pure software-based baseline system, while the word accuracy rate is preserved at 93.33%. As a part of the recognizer, a new adaptive beam-pruning algorithm is also proposed and implemented, which further reduces the average real-time factor to 0.54 with the word accuracy rate of 93.16%. The proposed speech recognizer is suitable for integration in various types of voice (speech)-controlled applications.

An implementation of Artificial-Neural-Network (ANN) based speech recog-nition systems on the embedded platform is explored in this paper. An FPGA chip is adopted as the hardware of the embedded platform with the architecture of SOC. This makes the speech recognition systems applicable on the voice activated systems in toys, games, smart phones, office devices, vehicular communications, etc. Because the fast Fourier transform (FFT) is an important operation in speech recognition, in which a great deal of floating-point arithmetic operations are performed, it takes a long time for speech recognition due to the limitation on the computation capability of the embedded platform. In this paper, we use Integer FFT to replace the Floating FFT so that the speed of speech recognition is enhanced without influencing the recognition rate. The ex-perimental results from the FPGA platform reveal that the speech recognition rate of the proposed hardware implementation methods is better than that ...

This paper presents exploration of speech enable operating systems, software, and applications. It begins with a description of how such systems work, and the level of accuracy that can be expected. It explains the applications of speech recognition technology in different areas education, medical, mobile computing, railway reservation, dictation, and web browsing. A brief comparison of the operating systems supported for voice, speech recognition software or tool. It gives the brief introduction about the potential of voice/speech recognition software. It explains the feature of different speech enable Operating system and speech recognition software. Windows speech recognition have many innovative features for Windows operating system and efficiently assist the computer to control, dictate, navigate, selecting the words, sending emails and correcting the words or sentences. It also explains the benefits and issue related to speech technology. In last era speech recognition technology grew tremendously. There are large number of companies who are working in these area and developing software for the people who are not able to control the system through keyboard or mouse such as physically impaired and senior citizens. This paper gives a brief introduction of speech enabled OS and speech recognition software.